Multivariate spatio-temporal models with latent dynamics for cardiovascular disease prediction with heterogeneous factors

Out-of-hospital cardiac arrest (OHCA), acute coronary syndrome (ACS), and stroke are the leading causes of out-of-hospital deaths. These are all time-sensitive cardiovascular diseases (CVDs), i.e. prompt access to the patient and early intervention significantly affect immediate outcome, survival, and neurological recovery. The main objective of this research is to develop novel realistic and interpretable statistical models and algorithms that, integrating diverse sources of data, aim to reduce the time to intervention of first responders and ambulance arrival, ultimately leading to a significant mortality reduction and improved neurological outcomes. This wide-ranging goal will be achieved by identifying, thanks to the developed methodologies, high-risk areas and by timely predicting, with accuracy and uncertainty quantification, areas of expected future higher risk. More specifically, we will first estimate and predict the OHCA risk for the municipalities of Ticino by designing a model which accounts for temporal and spatial heterogeneity, space-time interactions, inflation of zero events, demographic features, climate and environmental factors, and with careful prior elicitation. The scalable Bayesian space-time statistical models will be calibrated on different (large and heterogeneous) data sources and will be estimated by the INLA methodology. Furthermore, we suggest the methodological extension of the space-time model to a hidden Markov random field of dynamically varying dimension, with the aim of clustering municipalities over time in a reduced number of groups having comparable cardiovascular risk level. Subsequently, we will extend the model to jointly include other time-sensitive CVDs such as ACS and stroke. Because OHCA may be the presenting disease of ACS or of a massive stroke event, the question arises as of whether the spatio-temporal distributions of ACS and stroke are similar to the distribution of OHCA, and if ACS or stroke events can be used as proxy to forecast OHCA.